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1、Low-QualityProductReviewDetectioninOpinionSummarizationJingjingLiuYunboCaoNankaiUniversityMicrosoftResearchAsiaTianjin,ChinaBeijing,Chinav-jingil@microsoft.comyucao@microsoft.comChin-YewLinYalouHuangMingZhouMicrosoftResearchAsiaNankaiUniversityMicrosoftResearchAsiaBeijing,ChinaTianji
2、n,ChinaBeijing,Chinacyl@microsoft.comhuangyl@nankai.edu.cnmingzhou@microsoft.com1AmazonallowsuserstovoteforthehelpfulnessAbstractofeachreviewandthenranksthereviewsbasedontheaccumulatedvotes.However,accordingtoourProductreviewspostedatonlineshoppingsurveyinSection3,users’votesatAmazon
3、havesitesvarygreatlyinquality.Thispaperad-threekindsofbiasesasfollows:(1)imbalancevotedressestheproblemofdetectinglow-bias,(2)winnercirclebias,and(3)earlybirdbias.qualityproductreviews.Threetypesofbi-Existingstudies(Kimetal,2006;ZhangandVa-asesintheexistingevaluationstandardofradaraj
4、an,2006)usedtheseusers’votesfortrain-productreviewsarediscovered.Toassessingrankingmodelstoassessthequalityofreviews,thequalityofproductreviews,asetofspe-whichthereforearesubjecttothesebiases.cificationsforjudgingthequalityofre-Inthispaper,wedemonstratetheaforemen-viewsisfirstdefined
5、.Aclassification-tionedbiasesanddefineastandardspecificationtobasedapproachisproposedtodetectthemeasurethequalityofproductreviews.Wethenlow-qualityreviews.Weapplythepro-manuallyannotateasetofground-truthwithrealposedapproachtoenhanceopinionsum-worldproductreviewdataconformingtothespe
6、ci-marizationinatwo-stageframework.Ex-fication.perimentalresultsshowthattheproposedToautomaticallydetectlow-qualityproductre-approacheffectively(1)discriminateslow-views,weproposeaclassification-basedapproachqualityreviewsfromhigh-qualityonesandlearnedfromtheannotatedground-truth.The
7、pro-(2)enhancesthetaskofopinionsummari-posedapproachexploresthreeaspectsofproductzationbydetectingandfilteringlow-reviews,namelyinformativeness,readability,andqualityreviews.subjectiveness.Weapplytheproposedapproachtoopinion1Introductionsummarization,atypicalopinionminingtask.TheInth
8、epastfewyear